print.Ckmeans.1d.dp: Print Optimal Univariate Clustering Results

Description Usage Arguments Details Value Author(s) Examples

View source: R/print.R

Description

Print optimal univariate clustering results obtained from Ckmeans.1d.dp or Ckmedian.1d.dp.

Usage

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## S3 method for class 'Ckmeans.1d.dp'
print(x, ...)
## S3 method for class 'Ckmedian.1d.dp'
print(x, ...)

Arguments

x

object returned by calling Ckmeans.1d.dp or Cksegs.1d.dp.

...

arguments passed to print function.

Details

Function print.Ckmeans.1d.dp and print.Ckmedian.1d.dp prints the maximum ratio of between-cluster sum of squares to total sum of squares unless all input elements are zero. The ratio is an indicator of maximum achievable clustering quality given the number of clusters: 100% for a perfect clustering and 0% for no cluster patterns.

Value

An object of class "Ckmeans.1d.dp" or "Ckmedian.1d.dp" as defined in Ckmeans.1d.dp.

Author(s)

Joe Song and Haizhou Wang

Examples

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# Example: clustering data generated from a Gaussian
#          mixture model of two components
x <- c(rnorm(15, mean=-1, sd=0.3),
       rnorm(15, mean=1, sd=0.3))
res <- Ckmeans.1d.dp(x)
print(res)

res <- Ckmedian.1d.dp(x)
print(res)

y <- (rnorm(length(x)))^2
res <- Ckmeans.1d.dp(x, y=y)
print(res)

res <- Ckmedian.1d.dp(x)
print(res)

Example output

Cluster centers:
[1] -0.9714069  0.8864044

Cluster index associated with each element:
 [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

Within-cluster sum of squares:
[1] 1.0089259 0.5733426
Ckmeans.1d.dp returns 2 optimal clusters of sizes 15, 15 
  minimum total within-cluster sum of squares: 1.582268 
  maximum between-cluster sum of squares: 25.88597 
  total sum of squares of input vector: 27.46824 
  maximum (between-SS / total-SS): 94.2 %

Available components:
 [1] "cluster"      "centers"      "withinss"     "size"         "totss"       
 [6] "tot.withinss" "betweenss"    "BIC"          "xname"        "yname"       

Cluster centers:
[1] -0.9564509  0.8543187

Cluster index associated with each element:
 [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

Within-cluster sum of L1 distances:
[1] 3.104439 2.372098
Ckmedian.1d.dp returns 2 optimal clusters of sizes 15, 15 

Available components:
 [1] "cluster"      "centers"      "withinss"     "size"         "totss"       
 [6] "tot.withinss" "betweenss"    "BIC"          "xname"        "yname"       

Cluster centers:
[1] -1.0075150  0.8098013  1.1810913

Cluster index associated with each element:
 [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 3 2 3 3 2 2 2 2 2 2 2 2 3 2

Within-cluster sum of squares:
[1] 0.36275799 0.10881997 0.01179795
Ckmeans.1d.dp returns 3 optimal clusters of sizes 16.1139213135245, 14.0745503940078, 6.2651594971884 
  minimum total within-cluster sum of squares: 0.4833759 
  maximum between-cluster sum of squares: 34.14646 
  total sum of squares of input vector: 34.62983 
  maximum (between-SS / total-SS): 98.6 %

Available components:
 [1] "cluster"      "centers"      "withinss"     "size"         "totss"       
 [6] "tot.withinss" "betweenss"    "BIC"          "xname"        "yname"       

Cluster centers:
[1] -0.9564509  0.8543187

Cluster index associated with each element:
 [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

Within-cluster sum of L1 distances:
[1] 3.104439 2.372098
Ckmedian.1d.dp returns 2 optimal clusters of sizes 15, 15 

Available components:
 [1] "cluster"      "centers"      "withinss"     "size"         "totss"       
 [6] "tot.withinss" "betweenss"    "BIC"          "xname"        "yname"       

Ckmeans.1d.dp documentation built on July 22, 2020, 5:09 p.m.